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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Uncertainty-Induced Transferability Representation for Source-Free Unsupervised Domain Adaptation.

Jiangbo Pei, Zhuqing Jiang, Aidong Men

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |April 8, 2023
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    Summary

    This study introduces a new method for source-free unsupervised domain adaptation (SFUDA) that measures knowledge transferability without source data or target labels. The proposed approach enhances model adaptation by calibrating source knowledge and target semantics for improved performance.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Source-free unsupervised domain adaptation (SFUDA) aims to adapt models using unlabeled target data and existing source models.
    • Current SFUDA methods often fail to measure source knowledge transferability, leading to suboptimal performance and unreliable target semantics.
    • Existing transferability metrics require source data or target labels, which are unavailable in the SFUDA setting.

    Purpose of the Study:

    • To develop a novel approach for measuring knowledge transferability in SFUDA without requiring source data or target labels.
    • To introduce a framework that calibrates source knowledge and target semantics for safer and more effective domain adaptation.
    • To improve the performance of models in SFUDA tasks by addressing the limitations of existing methods.

    Main Methods:

    • Proposed Uncertainty-induced Transferability Representation (UTR) to analyze channel-wise transferability of the source encoder using uncertainty.
    • Developed a Calibrated Adaptation Framework (CAF) incorporating source knowledge and target semantics calibration modules.
    • UTR analyzes domain-level transferability and instance-level semantic reliability in the absence of source data and target labels.

    Main Results:

    • The proposed UTR effectively measures the transferability of source encoder channels to the target domain.
    • The CAF successfully guides the target model to learn transferable source knowledge and discard non-transferable information.
    • Experimental results demonstrate state-of-the-art performance on three SFUDA benchmarks, validating the method's effectiveness.

    Conclusions:

    • The novel UTR and CAF provide an effective solution for SFUDA by enabling reliable knowledge transfer and semantic calibration.
    • The method overcomes the limitations of existing transferability measurements by operating without source data or target labels.
    • The approach offers a significant advancement in SFUDA, achieving superior performance and safer model adaptation.